Thermophysical Change Detection on the Moon with the Lunar Reconnaissance Orbiter Diviner sensor

The Moon is an archive of the history of the Solar System, as it has recorded and preserved physical events that have occurred over billions of years. NASA’s Lunar Reconnaissance Orbiter (LRO) has been studying the lunar surface for more than 13 years, and its datasets contain valuable information about the evolution of the Moon. However, the vast amount and heterogeneous nature of data collected by LRO make the extraction of scientific insights very challenging - in the past most analyses relied on human review. Here, we present NEPHTHYS, an automated solution for discovering thermophysical changes on the surface using one of LRO’s largest datasets: the thermal data collected by its Diviner instrument. Specifically, NEPHTHYS is able to perform systematic, efficient, and large-scale change detection of present-day impact craters on the surface. Further work could enable more comprehensive studies of lunar surface impact flux rates and surface evolution rates, providing critical new information for future missions

Automated astronaut traverses with minimum metabolic workload : Accessing permanently shadowed regions near the lunar south pole

Altres ajuts: acords transformatius de la UABThe Artemis exploration zone is a topographically complex impact-cratered terrain. Steep undulating slopes pose a challenge for walking extravehicular activities (EVAs) anticipated for the Artemis III and subsequent missions. Using 5 m/pixel Lunar Orbiter Laser Altimeter (LOLA) measurements of the surface, an automated Python pipeline was developed to calculate traverse paths that minimize metabolic workload. The tool combines a Monte Carlo method with a minimum-cost path algorithm that assesses cumulative slope over distances between a lander and stations, as well as between stations. To illustrate the functionality of the tool, optimized paths to permanently shadowed regions (PSRs) are calculated around potential landing sites 001, nearby location 001(6), and 004, all within the Artemis III 'Connecting Ridge' candidate landing region. We identified 521 PSRs and computed (1) traverse paths to accessible PSRs within 2 km of the landing sites, and (2) optimized descents from host crater rims into each PSR. Slopes are limited to 15° and previously identified boulders are avoided. Surface temperature, astronaut body illumination, regolith bearing capacity, and astronaut-to-lander direct view are simultaneously evaluated. Travel times are estimated using Apollo 12 and 14 walking EVA data. A total of 20 and 19 PSRs are accessible from sites 001 and 001(6), respectively, four of which maintain slopes <10°. Site 004 provides access to 11 PSRs, albeit with higher EVA workloads. From the crater rims, 94 % of PSRs can be accessed. All round-trip traverses from potential landing sites can be performed in under 2 h with a constant walk. Traverses and descents to PSRs are compiled in an atlas to support Artemis mission planning

Discovery of a Dust Sorting Process on Boulders Near the Reiner Gamma Swirl on the Moon

In a database of lunar fractured boulders (Rüsch & Bickel, 2023, https://doi.org/10.3847/psj/acd1ef), we found boulders with reflectance features dissimilar to previously known morphologies. We performed a photo-geologic investigation and determined that the features correspond to a dust mantling on top of boulders with a unique photometric behavior. We next performed a photometric model inversion on the dust mantling using Bayesian inference sampling. Modeling indicates that the dust photometric anomaly is most likely due to a reduced opposition effect, whereas the single scattering albedo is not significantly different from that of the nearby background regolith. This implies a different structure of the dust mantling relative to the normal regolith. We identified and discussed several potential processes to explain the development of such soil. None of these mechanisms can entirely explain the multitude of observational constraints unless evoking anomalous boulder properties. Further study of these boulders can shed light on the workings of a natural dust sorting process potentially involving dust dynamics, a magnetic field, and electrostatic dust transport. The presence of these boulders appears to be limited to the Reiner K crater near the Reiner Gamma magnetic and photometric anomaly. This close spatial relationship further highlights that poorly understood processes occur in this specific region of the Moon

Extreme Low-Light Environment-Driven Image Denoising over Permanently Shadowed Lunar Regions with a Physical Noise Model

Recently, learning-based approaches have achieved impressive results in the field of low-light image denoising. Some state of the art approaches employ a rich physical model to generate realistic training data. However, the performance of these approaches ultimately depends on the realism of the physical model, and many works only concentrate on everyday photography. In this work we present a denoising approach for extremely low-light images of permanently shadowed regions (PSRs) on the lunar surface, taken by the Narrow Angle Camera on board the Lunar Reconnaissance Orbiter satellite. Our approach extends existing learning-based approaches by combining a physical noise model of the camera with real noise samples and training image scene selection based on 3D ray tracing to generate realistic training data. We also condition our denoising model on the camera’s environmental metadata at the time of image capture (such as the camera’s temperature and age), showing that this improves performance. Our quantitative and qualitative results show that our method strongly outperforms the existing calibration routine for the camera and other baselines. Our results could significantly impact lunar science and exploration, for example by aiding the identification of surface water-ice and reducing uncertainty in rover and human traverse planning into PSRs

Using Machine Learning to Reduce Observational Biases When Detecting New Impacts on Mars

The current inventory of recent (fresh) impacts on Mars shows a strong bias towards areas of low thermal inertia. These areas are generally visually bright, and impacts create dark scours and rays that make them easier to detect. It is expected that impacts occur at a similar rate in areas of higher thermal inertia, but those impacts are under-detected. This study investigates the use of a trained machine learning classifier to increase the detection of fresh impacts on Mars using CTX data. This approach discovered 69 new fresh impacts that have been confirmed with follow-up HiRISE images. We found that examining candidates partitioned by thermal inertia (TI) values, which is only possible due to the large number of machine learning candidates, helps reduce the observational bias and increase the number of known high-TI impacts.Comment: 17 pages, 10 figures, 2 tables (Author's preprint, accepted version

Cryogeomorphic Characterization of Shadowed Regions in the Artemis Exploration Zone

The Artemis program will send crew to explore the south polar region of the Moon, preceded by and integrated with robotic missions. One of the main scientific goals of future exploration is the characterization of polar volatiles, which are concentrated in and near regions of permanent shadow. The meter-scale cryogeomorphology of shadowed regions remains unknown, posing a potential risk to missions that plan to traverse or land in them. Here, we deploy a physics-based, deep learning-driven post-processing tool to produce high-signal and high-resolution Lunar Reconnaissance Orbiter Narrow Angle Camera images of 44 shadowed regions larger than ∼40 m across in the Artemis exploration zone around potential landing sites 001 and 004. We use these images to map previously unknown, shadowed meter-scale (cryo)geomorphic features, assign relative shadowed region ages, and recommend promising sites for future exploration. We freely release our data and a detailed catalog of all shadowed regions studied

Artificial Intelligence for the Advancement of Lunar and Planetary Science and Exploration

AI-driven methods have potential to minimise manual labour during planetary data processing and aid ongoing missions with real-time data analysis. This white paper focuses on key areas of AI-driven research, the need for open source training data, and the importance of collaboration between academia and industries to advance AI-driven research

Science Opportunities offered by Mercury's Ice-Bearing Polar Deposits

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